Socioeconomic status and COVID‐19‐related cases and fatalities in the world: A cross‐sectional ecological study

Abstract Background and Aims The COVID‐19 pandemic poses an extraordinary threat to global public health. We designed an ecological study to explore the association between socioeconomic factors and the COVID‐19 outcomes in 184 countries, using the geographic map and multilevel regression models. Methods We conducted a cross‐sectional ecological study in 184 countries. We performed regression analysis to assess the association of various socioeconomic variables with COVID‐19 outcomes in 184 countries, using ordinary least squares and multilevel modeling analysis. We performed two‐level analyses with countries at Level 1 and geographical regions at Level 2 in multilevel modeling analysis, using the same set of predictor variables used in ordinary least squares. Results There was a significant relationship between COVID‐19 cases rate (Log) per 100,000 inhabitants‐day at risk with human development index (HDI), percentage of the urban population, unemployment, and cardiovascular disease prevalence. The results displayed that the variances are varied between Level 1 (country level) and Level 2 (World Health Organization [WHO] regions), meaning that the geographic distribution represented a proportion of the changes in the COVID‐19 outcomes. Conclusion The study suggests that in addition to the socioeconomic status affects the COVID‐19 outcomes, countries' geographical location makes a part of changes in outcomes of diseases. Therefore, health policy‐makers could overcome morbidity and mortality in COVID‐19 by controlling the socioeconomics factors.

Due to the high rate of transmission by COVID-19, it is essential to identify the influential factors for the morbidity and mortality of the disease. Studies have shown that individual factors such as age, sex, and underlying diseases, including diabetes, hypertension, cardiovascular disease, chronic obstructive pulmonary disease (COPD), kidney failure, and cancer, are significant predictors for COVID-19-related outcomes, that is, incidence, mortality, and fatality. [6][7][8][9] In this regard, Yang et al. 10 conducted a meta-analysis, including eight studies with 46,248 COVID-19 patients. They indicated that the most prevalent comorbidities were hypertension (17%), diabetes (8%), cardiovascular disease (5%), and respiratory system disease (2%). Also, the results revealed that the odds ratio of hypertension, respiratory system disease, cardiovascular disease in severe patients was 2.36, 2.46, and 3.42, respectively, compared to nonsevere patients.
Besides, some socioeconomics and environmental factors might play a facilitating role in populations' susceptibility and vulnerability.
Studies have shown a significant relationship between  and socioeconomics variables such as public transportation per capita, aging index, poverty index, employment rate, gross domestic product (GDP) per capita, and the workforce employed in essential services. [11][12][13][14] An analysis of 3.99 million individuals with  in Brazil revealed a significant association between social and income inequalities and the COVID-19 mortality rate. 15 Wildman reported that a 1% increase in the Gini coefficient in the OECD countries is associated with an approximately 4% and 5% increase in incidence and mortality rate per million, respectively. 14 Although, there were some studies related to the role of socioeconomic status and demographic factors on the COVID-19related cases and fatalities. However, these studies were only conducted in a particular country or region, the data were obtained during the early phase of the pandemic, and disease was not reported in many countries. Therefore, this ecological study aimed to explore the association between socioeconomic factors and the COVID-19 outcomes in 184 countries, using the geographic map and multilevel regression models.

| Study population
We conducted a cross-sectional ecological study in 184 countries around the globe. We selected the countries based on the following criteria; first, up to February 12, 2021, at least one confirmed case of COVID-19 has been reported. Second, the data on COVID-19 and socioeconomic variables were available to them.

| Variables and data sources
We considered the data related to COVID-19 as outcome variables and socioeconomic factors as independent variables. The daily data on recorded cases and deaths by COVID-19 are summed up to February 12, 2021 from WHO reports for selected countries. 5 We estimated population-time at risk as a product of the total population multiplied by the number of days since the first symptom for the first confirmed case at each country. Then, the cumulative incidence and mortality rate was calculated using the populationtime at risk as a denominator. We also computed the case fatality rate by dividing the number of COVID-19 deaths by the confirmed cases.
We retrieved the data regarding socioeconomic variables such as human development index (HDI), total population, women population (% total population), the population aged over 65 years (% total population), the population aged over 75 years (% total population), population density (number per km 2 ), urban population (% total population), median age (year), total unemployment rate (%), years of schooling (years), and education index from reports of United Nations Development Program (UNDP). The education index ran from zero to one, an average of mean years of schooling (of adults) and expected years of schooling (of children). The data on the GDP per capita and health expenditure per capita was collected from World Bank data.
We obtained data about the prevalence of cardiovascular, diabetes and kidney, and chronic obstructive pulmonary disease (COPD) per 100,000 population using the global burden disease study in 2019.

| Statistical analysis
We first described the COVID-19 incidence and mortality rates, using the mean, standard deviation, median, and interquartile range (IQR) per 100,000 population-time at risk. The fatality rate was also reported as the percentage. Besides, the socioeconomic determinants and disease burden variables such as cardiovascular, diabetes and kidney, COPD were reported. We depicted the distribution of COVID-19-related variables by employing the shapefiles of countries worldwide.
We used Pearson's and Spearman's correlation coefficients to quantify the strengths of associations between morbidity, mortality, and case fatality rate with covariate variables. The outcome variables, that is, morbidity, mortality, and fatality rate, were transformed into the common logarithm (log 10 ) to adjust for the normal distribution. The death number was zero in 10 countries because zero cannot be transformed to the common logarithm, so we added 0.00001 per inhabitants-day at risk to the mortality rate. A two-sided t-test was applied to evaluate significant differences between COVID-19 outcomes and socioeconomic characteristics.
We performed regression analysis to assess the association of various socioeconomic variables with COVID-19 outcomes in 184 countries. Variables were selected as independent variables with a correlation coefficient greater than 0.5. We first used the ordinary least squares regression model as follows: Second, some of the data had a nested structure. There is usually collinearity in data with a hierarchical structure, so we applied the mixed model analysis to avoid this problem. Third, we performed a sensitivity analysis, running multilevel modeling analysis to examine whether the effects of socioeconomic variables on the COVID-19 variables were adjusted.
In multilevel analysis, we performed two-level analyses with countries at Level 1 and geographical regions at Level 2, using the same set of predictor variables used in ordinary least squares. The multilevel regression equation used is as the following formula: Log(COVID_outcome )= + HDI + URP + UNE + CRD + DK + POP + + . 3 | RESULTS As is shown in Figure 1,   Abbreviations: COPD, chronic obstructive pulmonary disease; GDP, gross domestic product.

ACKNOWLEDGMENTS
The authors would like to thank the reviewers, whose useful and constructive criticism significantly improved the paper. The present article was funded by Mashhad University of Medical Sciences. The funding source had no involvement in study design; collection, analysis, and interpretation of data; writing of the report and the decision to submit the report for publication.

CONFLICTS OF INTEREST
The authors declare no conflicts of interest.

TRANSPARENCY STATEMENT
The corresponding author confirm that "manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned (and, if relevant, registered) have been explained".

DATA AVAILABILITY STATEMENT
The data that support the findings of this study are available from the corresponding author upon reasonable request.

ETHICS STATEMENT
The present article is extracted from the research project approved by the Mashhad University of Medical Sciences with Proposal ORCID Javad Javan-Noughabi http://orcid.org/0000-0001-7809-1377